This invention relates generally to Price and Promotion Response Analysis (PRA) system and method to provide fast and efficient forecasts with price optimization systems for business planning. More particularly, the present invention relates to a method for analyzing historic sales data in conjunction with modeling data to forecast product sales and profits under various price and promotion conditions for the purpose of detailed business planning.
For businesses, prices of various products need to be set. These prices may be set with the goal of maximizing profit or demand or for a variety of other objectives. Traditionally price setting may be performed by experienced business managers, by comparison to competitors' pricing, to maintain sales goals or through complex price optimization systems.
For pricing optimization systems there may be a myriad of factors considered for the generation of demand models. As a result, the function for forecasting demand may be very complex. Additionally, costs may be fixed or variable and may be dependent on demand. As a result, the function for forecasting costs may be very complex. For a chain of stores with tens of thousands of different products, forecasting costs and determining a function for forecasting demand are difficult.
Additionally, typical price optimization provides little information to the business manager as to what effect changes in price and promotional activity will have. Moreover, little to no information is available as to the accuracy of the optimization forecasts. This results in a gap in the business manager's strategic knowledge, thereby reducing the effectiveness of business planning.
Currently forecasts for changes in price, or price response, is very limited. These forecasts, where available, typically lack confidence measurements or configurability. Comparing different product base pricing is difficult in such systems. Moreover, promotional response data is even more limited than price response data. Such limited techniques for forecasting a range of price and promotional activity may greatly impede a business planner or manager's ability to effectively make business decisions, and are wholly inadequate to base business decisions upon as they do not provide confidence measurements.
For the typical business, the above systems are still too inaccurate, inaccessible, and intractable in order to be utilized effectively for price and promotion response analysis. Businesses, particularly those involving large product sets, would benefit greatly from the ability to have accurate price and promotion response analysis.
Additionally, data may reside in disparate systems, not all with the same organization. Thus, a typical business may not be able to effectively compile data for price and promotion response analysis. Therefore, there is a need to be able to efficiently combine information from appropriate sources for such analyses.
It is therefore apparent that an urgent need exists for an improved system and method for price and promotion response analysis that is accurate, rapid and efficient. This solution would replace current forecasting techniques with a system capable of providing configurable forecasts based upon ranges of price and promotion data; thereby increasing effectiveness in downstream business planning.